红外与毫米波学报2011，Vol.30Issue(3)：250-254,259,6.
一种改进的Laplacian SVM的SAR图像分割算法
An improved Laplacian SVM algorithm for SAR image segmentation
摘要
Abstract
When the number of labeled samples is limited, Laplacian SVM needs as many as possible unlabeled samples to improve the performance of classification.However, when the nu mber of unlabeled samples is large, the required time and space complexity would be unacceptable.In order to apply it to large-scale classification problems like SAR image segmentation, a new method for SAR image segmentation named as improved Laplacian support vector machine algorithm ( Improved Laplacian SVM) was proposed.Watershed algorithm was first used to decompose the original image into several small prototype blocks, and image features of each small prototype blocks were extracted as training samples.Then an improved Laplacian SVM algorithm was proposed to classify data sets.The proposed method was verified on three SAR images.The experiments show that the method not only improves the accuracy of segmentation but also greatly reduces the running time of Laplacian SVM algorithm for image segmentation.关键词
LapSVM算法/图像分割/分水岭算法/SAR图像Key words
Laplacian SVM/ image segmentation/ watershed algorithm/ SAR image分类
信息技术与安全科学引用本文复制引用
刘若辰,邹海双,张莉,张萍,焦李成..一种改进的Laplacian SVM的SAR图像分割算法[J].红外与毫米波学报,2011,30(3):250-254,259,6.基金项目
国家自然科学基金(60803098,60970067) （60803098,60970067）
国家教育部博士点基金(20070701022),高等学校学科创新引智计划(111计划)(B07048),陕西省自然科学基金(2010JM8030) （20070701022）
